Text Generation
Transformers
causal-lm
linear-attention
rwkv
reka
knowledge-distillation
multilingual
Instructions to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="OpenMOSE/HRWKV7-Reka-Flash3-Preview")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenMOSE/HRWKV7-Reka-Flash3-Preview", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "OpenMOSE/HRWKV7-Reka-Flash3-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMOSE/HRWKV7-Reka-Flash3-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/OpenMOSE/HRWKV7-Reka-Flash3-Preview
- SGLang
How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "OpenMOSE/HRWKV7-Reka-Flash3-Preview" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMOSE/HRWKV7-Reka-Flash3-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "OpenMOSE/HRWKV7-Reka-Flash3-Preview" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "OpenMOSE/HRWKV7-Reka-Flash3-Preview", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use OpenMOSE/HRWKV7-Reka-Flash3-Preview with Docker Model Runner:
docker model run hf.co/OpenMOSE/HRWKV7-Reka-Flash3-Preview

- Xet hash:
- 838f4812cf69000b78f9ebe54e4f3f52cca1a47a90c0d13be3735d4f22fa188b
- Size of remote file:
- 1.19 MB
- SHA256:
- 7bb0fb99216cd1c6c148b5206b09d745850ac136dd9293f322e5480a0f036200
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